Extreme Optimization for Your Linux HPC Workloads

HPC Linux Expertise

FULL SCOPE OF EXPERTISE

DML-HPC Company

Company founded in 2022

Our founder is a top HPC expert with over +30 years of experience in extreme performance tuning in all HPC domains and senior architect in EMEA for more than 10 years; former DT at HPE.

We are a boutique specialist in code optimization for high-performance Linux environments. We help labs, scale-ups, research institutions, and enterprises achieve massive efficiency gains on existing hardware — without buying new servers.

Real-world impact: up to 10x speedups routinely on critical kernels and workloads through deep low-level tuning, algorithmic redesign, vectorization, memory hierarchy mastery, and energy-aware transformations.

We operate worldwide, helping companies solve their most challenging HPC code optimization problems. We have already assisted dozens of major international groups across critical sectors including:

• Oil & Gas (exploration, reservoir simulation)

• CAD/CAE & Scientific Computing (CFD, physics, Chemistry)

• Banking & Finance (risk modeling, quant simulations)

• Weather & Climate Modeling

1. PERFORMANCE DIAGNOSTIC & CODE EVALUATION

2. PRE-PURCHASE INFRASTRUCTURE BENCHMARKING

   STRATEGIC ADVISORY

3. CLASSIC & ADVANCED CODE OPTIMIZATION
   ROUTINELY DELIVERING 3× to 10× SPEEDUPS

4. ADVANCED TUNING TRAINING

   METHODOLOGY TRANSFER

5. CODE TRANSFORMATION & PLATFORM MIGRATION
   

Our Services

Experts in HPC Optimization, Benchmarks, and Tailored Training

Performance Diagnostic & Code Evaluation

In-depth profiling and assessment of your existing codes to identify bottlenecks, quantify inefficiencies (compute, memory, I/O, IB fabric), and precisely estimate achievable speedups and energy savings.

Benchmarking

Structured reflection, custom benchmark design, and performance modeling before investing in new hardware — ensuring the best fit for your workloads (Intel / AMD / NEC CPUs, GPU accelerators, hybrid architectures).

Deep low-level tuning including vectorization, loop transformations, cache optimization,bindings, kernel settings, storage, fabric, and algorithmic improvements — routinely delivering 3× to 10× speedups on large workloads. Economy driven strategies.

Classic & Advanced Code Optimization

Advanced Tuning Training & Methodology

Energy-Efficient Code Optimization

Code Transformation & Platform Migration

Hands-on advanced training sessions covering performance engineering best practices, profiling & tuning tools, code instrumentation, reproducible optimization workflows, and transfer of cutting-edge methodologies to your teams.

Porting and refactoring of codes across hardware platforms: Intel, AMD, ARM, CPU-to-GPU/accelerator , adaptation to multi-core / many-core / GPU architectures, and modernization of legacy Fortran/C/C++ HPC applications.

Power-aware code transformations: dynamic voltage and frequency scaling exploitation, precision reduction, memory access pattern optimization, algorithmic changes for lower power draw — significantly reducing energy consumption and TCO in large-scale data centers.

Advanced Tuning Training & Methodology

The DML-HPC Optimization Approach

HPC Performance Structural Analysis & Code Cleanup

1. ObjectiveTargeted intervention on HPC codebases (CPU, MPI, OpenMP, Fortran/C++/ASM) aimed at:

• Characterizing the actual low-level runtime behavior

• Modeling the theoretical peak performance achievable on the target platform

• Identifying the dominant structural inefficiencies

• Closing the gap between observed and potential performance

This service is designed for environments where performance represents a measurable technical and economic challenge.

2. Technical Scope

The analysis is based on:

• Low-level profiling (validated cycles, instructions, hardware signals)

• Calibrated micro-benchmarks

• Experimental correlation (code architecture)

• Modeling of the dominant execution regime (compute-bound, memory-bound, synchronization, dependency)

Hardware performance counters are used critically and validated through controlled experimentation
The goal is to observe the true execution reality, rather than interpreting isolated metrics.

3. Methodology - Phase 1 – Pre-Study (≤ 1 week)

Prerequisites:

• Source code frozen

• Dataset fixed and representative

• Numerical validation criterion clearly defined (bit-exact or specified tolerance)

• Dedicated and stable target platform

• Full access to hardware performance counters

Work performed:

• Low-level profiling of the existing code

• Identification of the dominant execution regime

• Modeling of the theoretical performance ceiling on the target platform

• Estimation of short-term achievable gain

• Identification of the main leverage points

Deliverable: Concise report including:

• Current performance position

• Estimated theoretical ceiling

• Measured performance gap

• Prioritized action plan

• Estimated effort and probability of success per lever

If immediately exploitable quick wins are identified, they can be implemented during this phase

The pre-study is invoiced independently of the outcome

3. Methodology - Phase 2 – Targeted Intervention (weeks)

If the pre-study reveals significant leverage:

• Measurable objective defined (benchmark, dataset, time-based metric)

• Platform frozen

• Validation criterion fixed

The intervention targets a quantified goal (e.g. ×3, ×5, etc.)

//Performance-based compensation

3. Methodology - Phase 3 – Heavy Transformations (optional)

When the targeted gain requires:

• Algorithmic redesign

• Deep code restructuring

• Major memory layout overhaul

A specific plan is proposed, including effort estimation (e.g. 3 months), probability of success, and adapted contractual conditions

4. Technical Requirements

• Dedicated machine (cloud or on-premise)

• Sufficient administrative / root-equivalent access

• Freedom to install profiling tools

• Stable environment throughout the analysis period

Without these conditions, results cannot be guaranteed

5. Positioning

This intervention does not aim at marginal fine-tuning. It targets major structural inefficiencies — often logical consequences of historical code evolution and the increasing complexity of modern architectures.

The goal is to restore coherence between:

• The execution model embodied in the code

• The actual micro-architectural reality of the processor

6. Commitment

Before any engagement, the following must be clearly established:

• Economic stakes of the computation

• Business impact of a significant performance gain

• Final decision-maker

In the absence of a clear business case, the intervention is not undertaken

7. Nature of the Approach

The approach is strictly observational. It relies on:

• Measurement

• Experimental correlation

• Identification of the dominant limiting factor

• Reduction of the gap to the physical ceiling

No assumptions are made a priori; the diagnosis is driven purely by observed execution reality